Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers

References

• Alcalá-Fdez , J. , Sánchez , L. , Garćia , S. , Jesus , M.J. , Ventura , S. , Garrell , J.M. , Otero , J. , Romero , C. , Bacardit , J. , Rivas , J.M. , Fernández , J.C. and Herrera , F. 2009 . KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems . Soft Computing , 13 ( 3 ) : 307 – 318 .
   Web of Science ®Google Scholar
• Alcalá-Fdez , J. , Fernandez , A. , Luengo , J. , Derrac , J. , Garćia , S. , Sánchez , L. and Herrera , F. 2011 . KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework . Journal of Multiple-Valued Logic and Soft Computing , 17 ( 2-3 ) : 255 – 287 .
   Web of Science ®Google Scholar
• Batista , G. , Prati , R. and Monard , M. 2004 . A study of the behaviour of several methods for balancing machine learning training data . SIGKDD Explorations , 6 ( 1 ) : 20 – 29 .
   Google Scholar
• Berger J. Statistical decision theory and Bayesian Analysis . Springer-Verlag .
   Google Scholar
• Bradley , A.P. 1997 . The use of the area under the ROC curve in the evaluation ofmachine learning algorithms . Pattern Recognition , 30 ( 7 ) : 1145 – 1159 .
   Web of Science ®Google Scholar
• Casillas , J. and Carse , B. 2009 . Genetic Fuzzy Systems: Recent Developments and Future Directions . Soft Computing , 13 ( 3 ) : 417 – 418 .
   Google Scholar
• Chawla , N.V. , Bowyer , K.W. , Hall , L.O. and Kegelmeyer , W.P. 2002 . SMOTE: Synthetic minority over-sampling technique . Journal of Artificial Intelligent Research , 16 : 321 – 357 .
   Web of Science ®Google Scholar
• Chawla , N.V. , Japkowicz , N. and Kolcz , A. 2004 . Editorial: Special issue on learning from imbalanced data sets . SIGKDD Explorations , 6 ( 1 ) : 1 – 6 .
   Google Scholar
• Chen , S.H. 1985 . Operations on fuzzy numbers with function principal . Tamkang Journal of Management Sciences , 6 ( 1 ) : 13 – 25 .
   Google Scholar
• Cheng , C.H. 1998 . A new approach for ranking fuzzy numbers by distance method . Fuzzy Sets and Systems , 95 : 307 – 317 .
   Web of Science ®Google Scholar
• Chen , S. J. and Chen , S. M. 2003 . A new method for handling multicriteria fuzzy decision making problems using FN-IOWA operators . Cybernetics and Systems , 34 : 109 – 137 .
   Web of Science ®Google Scholar
• Chen , S. J. and Chen , S. M. 2007 . Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers . Applied Intelligence , 26 ( 1 ) : 1 – 11 .
   Web of Science ®Google Scholar
• Chen , S. H. 1999 . “ Ranking generalized fuzzy number with graded mean integration ” . In Proceedings of the eighth international fuzzy systems association world congress 2 899 – 902 .
   Google Scholar
• Crockett K. , Bandar Z. , O'Shea J. On producing balanced fuzzy decision tree classifiers . IEEE Internat. Conf. on Fuzzy Systems , 1756 – 1762 2006 .
   Google Scholar
• Chu , T. C. and Tsao , C. T. 2002 . Ranking fuzzy numbers with an area between the centroid point and original point . Computers and Mathematics with Applications , 43 : 111 – 117 .
   Web of Science ®Google Scholar
• Dmochowski , J.P. , Sajda , P. and Parra , L.C. 2010 . Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximators, and Upper Bounds . Journal of Machine Learning Research , 11 : 3313 – 3332 .
   Google Scholar
• Elkan C. The Foundations of Cost-Sensitive Learning . Proceedings of the IJCAI-01 , 973 – 978 2001 .
   Google Scholar
• Fawcett , T. and Provost , F.J. 1997 . Adaptive fraud detection . Data Mining Knowledge Discovery , 1 ( 3 ) : 291 – 316 .
   Web of Science ®Google Scholar
• Fernández , A. , Garcia , S. , del Jesús , M.J. and Herrera , F. 2008 . A study of the behaviour of linguistic fuzzy rule based classification system in the framework of imbalanced data-sets . Fuzzy Sets and Systems , 159 : 2378 – 2398 .
   Web of Science ®Google Scholar
• Fernández , A. , del Jesús , M.J. and Herrera , F. 2009 . On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-sets . Expert Systems with Applications , 36 : 9805 – 9812 .
   Web of Science ®Google Scholar
• Fernández , A. , del Jesús , M.J. and Herrera , F. 2009 . Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets . International Journal of Approximate Reasoning , 50 : 561 – 577 .
   Web of Science ®Google Scholar
• Fernández , A. , del Jesús , M.J. and Herrera , F. 2010 . On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets . Information Sciences , 180 ( 8 ) : 1268 – 1291 .
   Web of Science ®Google Scholar
• Hart , P. 1968 . The condensed nearest neighbor rule . IEEE Trans. Inform. Theory , 14 : 515 – 516 .
   Web of Science ®Google Scholar
• He , H. and Garcia , E.A. 2009 . Learning from Imbalanced Data . IEEE Transactions on Knowledge and Data Engineering , 21 ( 9 ) : 1263 – 1284 .
   Web of Science ®Google Scholar
• Japkowicz , N. and Stephen , S. 2002 . The class imbalance problem: a systematic study . Intelligent Data Anal. , 6 ( 5 ) : 429 – 450 .
   Google Scholar
• Jain R. Decision-making in the presence of fuzzy variables , IEEE Trans. Systems Man and Cybernet. SMC-6 , 698 – 703 1976 .
   Google Scholar
• Jain , R. 1978 . A procedure for multi-aspect decision making using fuzzy sets . Internat. J. Systems Sci. , 8 : 1 – 7 .
   Web of Science ®Google Scholar
• Kilic , K. , Uncu , O. and Türksen , I.B. 2007 . Comparison of different strategies of utilizing fuzzy clustering in structure identification . Information Sciences , 177 ( 23 ) : 5153 – 5162 .
   Web of Science ®Google Scholar
• Kubat M. , Holte R. , Matwin S. Learning when Negative Examples Abound . European Conference on Machine Learning 1997 .
   Google Scholar
• Kubat M. , Matwin S. Addressing the curse of imbalanced training sets: one-sided selection . Internat. Conf. Machine Learning , 170 – 186 1997 .
   Google Scholar
• Laurikkala J. Improving identification of difficult small classes by balancing class distribution . T.R. A-2001-2 , University of Tampere 2001 .
   Google Scholar
• Liang C. , Wu J. , Zhang J. Ranking indices and rules for fuzzy numbers based on gravity center point . World Congress on Intelligent Control and Automation , Dalian , , China 2006 .
   Google Scholar
• Mazurowski , M. , Habas , P. , Zurada , J. , Lo , J. , Baker , J. and Tourassi , G. 2008 . Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance . Neural Networks , 21 ( 2-3 ) : 427 – 436 .
   PubMed Web of Science ®Google Scholar
• Murakami S. , Maeda S. , Imamura S. Fuzzy decision analysis on the development of centralized regional energy control system , IFAC Symp. on Fuzzy Inform. Knowledge Representation and Decision Anal ., 363 – 368 1983 .
   Google Scholar
• Nojima , Y. , Alcalá , R. , Ishibuchi , H. , Herrera , F. Special issue on evolutionary fuzzy systems . Soft Computing . doi: 10.1007/s00500-010-0663-2
   Google Scholar
• Palacios , A. , Couso , I. and Sánchez , L. 2011 . Future performance modeling in athletism with low quality data-based GFSs . Journal of Multivalued Logic and Soft Computing , 17 ( 2-3 ) : 207 – 228 .
   Google Scholar
• Palacios A. , Sánchez L. , Couso I. Diagnosis of dyslexia from vague data with Genetic Fuzzy Systems 51 , 993 – 1009 2010 .
   Google Scholar
• Peng , X. and King , I. 2008 . Robust BMPM training based on second-order cone programming and its application in medical diagnosis . Neural Networks , 21 ( 2-3 ) : 450 – 457 .
   PubMedGoogle Scholar
• Phua , C. , Alahakoon , D. and Lee , V. 2004 . Minority report in fraud detection: classification of skewed data . SIGKDD Explorations Newsletter , 6 ( 1 ) : 50 – 59 .
   Google Scholar
• Provost , F. and Fawcett , T. 1998 . “ Robust classification systems for imprecise enviroments ” . In Proc. AAAI 706 – 713 .
   Google Scholar
• Ramli , N. and Mohamad , D. 2009 . A comparative analysis of centroid methods in ranking fuzzy numbers . European Journal of Scientific Research , 28 ( 3 ) : 492 – 501 .
   Google Scholar
• Sánchez , L. , Otero , J. and Couso , I. 2009 . Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms . Soft Computing , 13 ( 5 ) : 467 – 479 .
   Google Scholar
• Shieh , B.S. 2007 . An approach to centroids of fuzzy numbers . International Journal of Fuzzy Systems , 9 ( 1 ) : 51 – 54 .
   Google Scholar
• Soler V. , Cerquides J. , Sabria J. , Roig J. , Prim M. Imbalanced datasets classification by fuzzy rule extraction and genetic algorithms . IEEE Internat. Conf. Data Mining –Workshops , 330 – 336 2006 .
   Google Scholar
• Sun , Y. , Wong , A.K.C. and Kemel , M. 2009 . Classification of imbalanced data: A review . International Journal of Pattern Recognition and Artificial Intelligence , 23 ( 4 ) : 687 – 719 .
   Web of Science ®Google Scholar
• Tomek , I. 1976 . Two modifications of cnn . IEEE Trans. Systems Man Comm. , 6 : 769 – 772 .
   Web of Science ®Google Scholar
• Visa S. , Ralescu A. Learning imbalanced and overlapping classes using fuzzy sets . Internat. Conf. Machine Learning –Workshop on Learning from Imbalanced Datasets II 2003 .
   Google Scholar
• Visa S. , Ralescu A. The effect of imbalanced data class distribution on fuzzy classifiers–experimental study . IEEE Internat. Conf. on Fuzzy Systems , 749 – 754 2005 .
   Google Scholar
• Wang , Y.J. and Lee , H.S. 2008 . The revised method of ranking fuzzy numbers with an area between the centroid and original points . Computers and Mathematics with Applications , 55 : 2033 – 2042 .
   Web of Science ®Google Scholar
• Weiss , G. 2004 . Mining with rarity: a unifying framework. SIGKDD Explorations , 6 ( 1 ) : 7 – 19 .
   Google Scholar
• Wilson , D.R. 1972 . Asymptotic properties of nearest neighbour rules using edited data . IEEE Trans. Systems Man Comm. , 2 ( 3 ) : 408 – 421 .
   Google Scholar
• Xu , L. , Chow , M. and Taylor , L. 2007 . Power distribution fault cause identification with imbalanced data using the data mining-based fuzzy classification e-algorithm . IEEE Trans. Power Systems , 22 ( 1 ) : 164 – 171 .
   Google Scholar